Entrance detection from street-level imagery

ABSTRACT

Architecture that detects entrances on building facades. In a first stage, scene geometry is exploited and the multi-dimensional problem is reduced down to a one-dimensional (1D) problem. Entrance hypotheses are generated by considering pairs of locations along lines exhibiting strong gradients in the transverse direction. In a second stage, a rich set of discriminative image features for entrances is explored according to constructed designs, specifically focusing on properties such as symmetry and color consistency, for example. Classifiers (e.g., random forest) are utilized to perform automatic feature selection and entrance classification. In another stage, a joint model is formulated in three dimensions (3D) for entrances on a given facade, which enables the exploitation of physical constraints between different entrances on the same facade in a systematic manner to prune false positives, and thereby select an optimum set of entrances on a given facade.

BACKGROUND

Urban scene understanding is an active area of research. Devoid of anycontext, entrance detection in outdoor scenes is extremely challenging.Scene clutter is a problem because entrances typically make up only asmall portion of the image of a building facade. Most entrances havedoors whereas some do not. Moreover, there is a wide variety of sizesand appearances of doors. Arches over doors, steps leading up to doors,transparent doors, reflective doors, doors with large handles, partiallyopen doors, shuttered doors etc., lead to large intra-class variation.Additionally, the camera view, occlusions due to trees, vehicles,people, and other objects in the scene further complicate the entrancedetection task.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some novel embodiments described herein. This summaryis not an extensive overview, and it is not intended to identifykey/critical elements or to delineate the scope thereof. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

The disclosed architecture detects entrances on building facades, whichis desirable within urban scene understanding. The architecture can berealized as a multistage system. In a first stage, scene geometry isexploited and the multi-dimensional problem is reduced down to aone-dimensional (1D) problem. Entrance hypotheses are generated byconsidering pairs of locations along lines exhibiting strong gradientsin the transverse direction. In a second stage, a rich set ofdiscriminative image features for entrances is explored according toconstructed designs, specifically focusing on properties such assymmetry and color consistency, for example. Classifiers (e.g., randomforest) are utilized to perform automatic feature selection and entranceclassification. In another stage, a joint model is formulated in threedimensions (3D) for entrances on a given facade, which enables theexploitation of physical constraints between different entrances on thesame facade in a systematic manner to prune false positives, and therebyselect an optimum set of entrances on a given facade.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of the various ways in which the principles disclosed hereincan be practiced and all aspects and equivalents thereof are intended tobe within the scope of the claimed subject matter. Other advantages andnovel features will become apparent from the following detaileddescription when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system in accordance with the disclosedarchitecture.

FIG. 2 illustrates an input image having an identified foreground facademask and ground line.

FIG. 3 illustrates a method in accordance with the disclosedarchitecture.

FIG. 4 illustrates an alternative method in accordance with thedisclosed architecture.

FIG. 5 illustrates yet another alternative method in accordance with thedisclosed architecture.

FIG. 6 illustrates a block diagram of a computing system that executesentrance detection from street-side images in accordance with thedisclosed architecture.

DETAILED DESCRIPTION

The disclosed architecture detects building entrances in outdoor scenes,which is a desirable component for urban scene understanding. Whileentrance detection in indoor scenes has received a lot of attention,tackling the problem in outdoor scenes is considerably more complicatedand remains largely unexplored. The wide variety of door appearances andgeometries, background clutter, occlusions, specularity, and otherdifficult lighting conditions together impose many difficult challenges.

The architecture can be realized as a multistage system. In a firststage, scene geometry is exploited and the multi-dimensional problem isreduced down to a one-dimensional (1D) problem. Entrance hypotheses aregenerated by considering pairs of locations along lines exhibitingstrong gradients in the transverse direction. In a second stage, a richset of discriminative image features for entrances is explored accordingto constructed designs, specifically focusing on properties such assymmetry and color consistency, for example. Classifiers (e.g., randomforest) are utilized to perform automatic feature selection and entranceclassification. In another stage, a joint model is formulated in threedimensions (3D) for entrances on a given facade, which enables theexploitation of physical constraints between different entrances on thesame facade in a systematic manner to prune false positives, and therebyselect an optimum set of entrances on a given facade.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form in order to facilitate adescription thereof. The intention is to cover all modifications,equivalents, and alternatives falling within the spirit and scope of theclaimed subject matter.

FIG. 1 illustrates a system 100 in accordance with the disclosedarchitecture. The system 100 can include an extraction component 102configured to extract entrance candidates 104 from images 106 of afacade and generate entrance hypotheses 108 based on the entrancecandidates 104. The images can correspond to a street-view of thefacade. A classification component 110 can be provided and configured toclassify the entrance candidates 104 into different classes todifferentiate facade entrances 112 from non-entrances 114 based on theentrance hypotheses 108. A multi-dimensional reasoning component 116 canbe provided and configured to project the candidates intomulti-dimensional space to resolve conflicts, ultimately enabling theselection of an optimum set of entrances for a given facade.

The extraction component 102 generates the entrance hypotheses 104 basedon the processing of pairs of facade locations (e.g., buildings, shops,etc.) along gradient lines in a transverse direction. The classificationcomponent 110 employs discriminative image features related toentrances. The features include entrance symmetry and entrance color.The entrance candidates 104 are specified by bounding polygons (e.g.,rectangles, circles, arcs, etc.) where polygon line segmentation for abounding rectangular box, for example, is determined using vertical andhorizontal edge detection.

The classification component 110 processes entrance candidates 104 fromdifferent images independently, and the multi-dimensional reasoningcomponent 116 resolves conflicts between different views of the entrancecandidates 104 using joint reasoning in 3D (three-dimensional) space.The classification component 110 classifies the candidate entrances 104based on groundtruth matching and an entrance or non-entrance label.

The system 100 can further comprise a modeling component 118 configuredto formulate a joint model 120 for entrances on a given facade. Thejoint model 120 enables the utilization of physical constraints betweendifferent entrances of a same facade to derive an optimum set ofentrances 122 for the given facade.

It is to be understood that in the disclosed architecture, certaincomponents may be rearranged, combined, omitted, and additionalcomponents may be included. Additionally, in some embodiments, all orsome of the components are present on the client, while in otherembodiments some components may reside on a server or are provided by alocal or remote service.

FIG. 2 illustrates an input image 200 having an identified foregroundfacade mask 202 and ground line 204. Street-side (street level) imagespredominantly cover the ground-floor region of the facades and containvarious background clutter making it difficult to distinguish amongwalls, windows, and doors, for example. The image 200 and mask 202 isone of a collection of images and masks that correspond approximately tothe first floor of the facade 206 (e.g., a building facade). Theforeground masks that approximate the frontal view facades and theground lines can be estimated via LiDar (a technology that measuresdistance by illuminating a target with a laser and analyzing thereflected light) data, for example. The mask 202 may also have a labelthat indicates to which building/facade it corresponds. The images mayalso be calibrated.

The image 200 shows that façade 206 having a first set of doors 208 anda second door 210 of the same facade 206 but of different businesses.After mask and ground-line computation, edgelet detection 212(extraction) is performed, which then enables the computation ofentrance candidates 214 (in thick bold lined bounding boxes) in a view216. Upper-level windows 218 and other clutter are no longer consideredin the architecture processing.

In a more detailed description, an input to the architecture (e.g., avision module) is a collection of images and masks correspondingapproximately to the first floor of a building. Each mask has a labelthat indicates to which building the mask corresponds. The images usedmay be calibrated.

In one implementation, the architecture for entrance detection comprisesthree stages. First, an entrance candidate extraction stage is executedthat generates entrance hypotheses at a high recall and low precision.Second, a classifier designed to separate entrances from other classesof clutter (e.g., windows, etc.) is executed to remove many possiblefalse positives (non-entrances) with negligible loss in recall. Lastly,the results from multiple detections in multiple views are combinedexploiting global constraints.

More specifically, entrance candidates (e.g., rectangular image patches)are identified based on edgelet distribution, features extracted, andindependent classification performed on each candidate. Theentrance-likelihood of all candidates produced by the classifier is thenprojected back to the 3D real-world space, where conflicts such asoverlapping entrances are resolved and final global decisions ofentrance locations are made by considering individual candidatesjointly.

With respect specifically to candidate extraction, an input to thearchitecture is street-view images with foreground masks of approximatedfrontal view facades as well as ground-lines. One or both of these canbe estimated via LiDar data.

Entrance candidates can be specified by rectangular bounding boxes andthen normalized into the same sized image patch before featureextraction. The left, right, and top boundaries of each bounding box foreach candidate are computed and the bottom boundary is selected as thefacade ground-line. Although not all entrances are perfect rectangles,most have straight lines on the left and right boundaries, which canthen be initially processed to derive potential entrance candidates.

Explicit line segment detection can be slow and sensitive to noise.Thus, assuming the input image contains an approximated frontal view ofthe facade, a more robust and efficient approach is used to detectvertical edgelets and then accumulate the edgelets vertically. Afteredge detection (e.g., using Canny edge detection that employs amulti-stage algorithm for edge detection in images), an edge pixel, withboth its neighboring pixels above and below being also edge pixels, isaccepted as a vertical edgelet. The binary vertical edgelet image E canbe efficiently calculated via the following pixel-AND operation,E _(v)(x,y)=E(x,y−1)

E(x,y)

E(x,y+1)  (1)where E(x,y) represents the binary edge image E, x is the facade baseline, y is the vertical coordinate, and

denotes pixel-wise intersection. A similar rule can be applied tohorizontal edgelets where the pixel neighborhood is defined left andright rather than above and below.

Peak extraction is performed on the edgelet distributions to extractpotential vertical boundaries. One possible approach uses aregion-of-dominance method. All local peaks are ranked according totheir associated region of dominance, and then the top peaks per image(e.g., twenty-five) selected as potential vertical boundaries.

To propose an entrance candidate, first, a pair of two nearby verticalboundaries is selected, horizontal edgelets between the chosen pair ofvertical boundaries are accumulated to obtain a horizontal edgeletdistribution, and the local peaks are extracted as the top boundary.Only candidates with a predefined width-to-height ratio (e.g., 0:2 to1:2) are accepted.

With respect to entrance classification, the set of entrance candidatesextracted usually has a high recall rate, that is, most of the trueentrances are covered by the candidates; however, outliers may alsoexist. Therefore, rich visual content features are extracted from allcandidates, and a classifier is trained for deleting candidates (rulingout outliers while keeping the inliers). During the training stage, eachcandidate c_(i) is matched to the groundtruth and associated with abinary label y_(i)ε{1,0} indicating a door or non-door candidate. Notethat multiple candidates can be matched to the same training example atthis stage.

The rectangular bounding box of each candidate aligns with edgehistogram peaks, and thus, typically indicates the segmentation betweenthe ‘doors’ (inside) and its ‘door frame’ (outside). It has been shownthat many discriminative features of entrances lie on the frame.Accordingly, the image patches can be extracted with an increased margin(e.g., a 20% margin) outside of the bounding boxes, and then featuresare extracted from the patches. One or more image features that can beemployed include, but are not limited to, Histogram of Gradient (HoG),Principal Component Analysis (PCA), Reflection Symmetry (Sym), and ColorStatistics (CS).

With respect to using the HoG feature, a 128×64 patch size segmentedinto 16-by-8 cells, can be used, resulting in 15-by-7 blocks and ninehistogram bins. PCA can be utilized to learn dominant eigenvector ofeigen-doors from the positive candidate patches, followed by extractingthe low dimensional reconstruction coefficients for both positive andnegative patches for a discriminative training. All possible imagepatches that represent entrances may be distributed within a much lowerdimensional space, compared to the original image space, or the space of‘non-entrance’ image patches.

Thus, a low-dimensional reconstruction of the door space is constructedwhile still considering the distribution of non-entrances for adiscriminative training and for improved performance. PCA is typicallysensitive to noise, outliers, and occlusions, which occur frequently instreet-side images and may not be effective in dealing with a largeblock of continuously corrupted pixels caused by occlusion (e.g., fromtrees, poles, cars on the street, etc.). To address occlusions,occlusions are simulated by a predefined set of occlusion masks (e.g.,nine). The masks can be applied during the dimensional reduction step.

The top coefficients (e.g., twenty-five) are extracted for eachforeground mask. Given a candidate patch under partial occlusion, aslong as the occlusion mask masks out most of the occluded region, thecorresponding low dimensional reconstructions can be considered asacceptable features.

Reflection symmetry is employed as feature for entrance detection. Foran open or transparent entrance (where the indoor contents are visible),the color distribution on the door frames remains consistent withleft-right symmetry. The RGB (red-green-blue) image patch is decomposedinto HSI (hue, saturation, intensity) channels and the symmetry featureextracted from each channel separately. Given the candidate image patch,a local scanning window can be applied under different scales (e.g.,8-by-8, 16-by-16, and 32-by-32), scanning through the left half of theimage, and extracting the local histogram. Additionally, the histogramcan also be extracted from the symmetric region on the right side, andthen the L2 Euclidean distance between the two histograms computed asthe symmetry feature.

Additionally, color statistic features (HSI channels separately) can beextracted by applying a similar scanning window and extracting the localmean and standard deviation of the pixel values.

Some examples of partial feature visualization can be obtained to show acandidate patch, a histogram of edge orientation, low-dimensionalrecovery from the principal components, local asymmetry score onintensity (I) channel, and local variance on the hue (H) channel. It canbe observed that entrances are usually more ‘symmetric’ thannon-entrances, and especially around the left and right boundaries.

A Random Forest visual classifier is then learned from extractedfeatures to produce either binary or soft decision for each candidatepatch independently. A Random Forest classifier is a method ofclassification that operates by constructing decision trees at trainingtime and outputting the class that is the mode of the classes output byindividual trees.

With respect to joint facade analysis, entrance candidates fromdifferent images and scored independently by the image-based classifiermay not form a plausible 3D solution. A 3D facade and its entrances aretypically visible in multiple views. Conflicts between different viewscan be resolved by performing joint reasoning on all detected entrancecandidates, in 3D. Analysis in 3D also enables the exploitation ofper-facade constraints such as the typical density of entrances on afacade.

Using calibration information, the facade boundaries and the entrancecandidates are back-projected to the 3D world. Given a 3D facade, and aset of entrance candidates that fall on that facade c={c_(n)|n=1, . . ., N}, entrance locations are jointly inferred, and specified as a binaryindicator z=z_(n), in which z_(n)ε{0,1}. Let O={o₁, . . . o_(N)} be theset of image observations corresponding to the candidates. According toBayes rule,P(z|O)∝P(z)P(O|z)  (2)where P(z) encodes prior knowledge and preferences such as the entrancedensity on the facade and non-overlapping constraint, and P(O|z) is thelikelihood term of obtaining the observations O given z.

For the prior term, the entrance density (the number of entrances permeter) can be modeled as a Gaussian distribution. A strictnon-overlapping constraint is applied between 3D entrances into theprior.

${P(z)} = \left\{ {\begin{matrix}{{0;{{\exists{\left( {i,j} \right){s \cdot t \cdot z_{i}}}} = 1}},{z_{j} = 1},{D_{ij}^{(h)} < \tau_{1}}} \\{{{P(z)} = {\aleph\left( {{\frac{{z}_{1}}{L}❘\mu},\sigma} \right)}};{otherwise}}\end{matrix}.} \right.$where L is the total length of the facade; μ and σ are the average andstandard deviation, respectively, of the entrance density learned fromthe data; and, D_(ij) is the horizontal real-world distance between thecenter of candidate i and j, where τ₁=1.5 (meter).

Making an independence assumption between observations,P(O)|z)=Π_(n) ^(N) P(o _(n) |z)  (3)where P(O)|z)=s_(n)t_(n), where s_(n)ε{P_(n) ^((tp)), P_(n) ^((fp))} isthe likelihood of obtaining the classification score s_(n) on the nthcandidate; P_(n) ^((tp)), P_(n) ^((fp)) is the probability that thevisual classifier makes a true positive, false positive classification,respectively; and, t_(n)ε{1, P_(n) ^((fn))} where P_(n) ^((fn)) is theprobability that the visual classifier makes a false positive.

In order to evaluate equation (3) above, a decision needs to be made foreach candidate whether it is a true positive, false positive, or falsenegative given the solution hypothesis z, that is, all candidates on thesame facade ({c_(i)|i=1, . . . , N) need to be matched to the set ofhypothesized entrances ({c_(j)|z_(j)=1). A match is found if D_(ij)^((h))<τ₀, in which τ₀ is a threshold indicating strong overlapping. Thethreshold τ₀ can be set to τ₀=0.5 meter <τ₁, for example.

The classifier generates soft detection scores s_(i) for each candidateindicating the detection confidence; thus, the classificationprobability can be assigned as,P _(n) ^((tp)) =s _(n) ^(α)  (4)P _(n) ^((fp))=1−s _(n) ^(α)  (5)where α adjusts the relative weighting between false positive and falsenegative in the final solution. If a candidate is selected by z_(n)=1,but no matching is found from another view where the candidate shouldalso be visible, a miss detection penalty can be applied, with a presetmiss-detection probability P_(n) ^((fn))=0.3.

Since the combinatorial optimization of,z*=arg max,P(z|O)  (6)is NP-hard (non-deterministic polynomial-time hard), a stochasticoptimization approach similar to Markov Chain Monte Carlo (MCMC) can beadopted. The optimal (Maximum-a-Posterior) solution of z is beingsought, rather than its posterior distribution. Three types of balancedlocal moves can be specified: Add an Entrance (AaE), Remove an Entrance(RaE), and Shift an Entrance (SaE). During the AaE move, a new candidatei is selected (z_(i)=0→z_(i)=1) under the non-overlapping constraint inEquation (3). On the other hand, an RaE move randomly eliminates ancandidate (z_(i)=1→z_(i)=0).

The SaE move substitutes a candidate locally with another contradictorycandidate (z_(i)=1, z_(j)=0→z_(i)=0, z_(j)=1|D_(ij) ^((h))<τ₁). AlthoughSaE move can be also realized via an RaE move followed by an AaE move,such a 2-move combination may be less likely to occur if the first moveof RaE significantly reduces the objective function score. A new movez*→z is accepted stochastically with the probability,

$\begin{matrix}{\min\left( {1,\frac{P\left( {z^{*}❘O} \right)}{P\left( {z❘O} \right)}} \right)} & (7)\end{matrix}$

As a general summary, the entrance detection problem is reduced to a 1Dscan. The architecture employs three stages with components forcandidate extraction, entrance classification, and 3D fusion. Candidateextraction detects as many candidates as possible necessitating a highrecall with low precision. Classification exploits several featurescharacteristic of doors/entrances such as reflection symmetry and colorstatistics. Fusion exploits and encodes physical constraints such as thetypical density of entrances on facades per unit length, non-overlap fordoors, etc. The problem can be modeled in a Bayesian sense, and theMarkov Chain Monte Carlo algorithm used for estimating the optimum setof doors that together explain a given facade image.

Included herein is a set of flow charts representative of exemplarymethodologies for performing novel aspects of the disclosedarchitecture. While, for purposes of simplicity of explanation, the oneor more methodologies shown herein, for example, in the form of a flowchart or flow diagram, are shown and described as a series of acts, itis to be understood and appreciated that the methodologies are notlimited by the order of acts, as some acts may, in accordance therewith,occur in a different order and/or concurrently with other acts from thatshown and described herein. For example, those skilled in the art willunderstand and appreciate that a methodology could alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all acts illustrated in a methodology maybe required for a novel implementation.

FIG. 3 illustrates a method in accordance with the disclosedarchitecture. At 300, street-side (street-level) images are received,each having a foreground mask and a ground-line predetermined. At 302,entrance candidates are specified using bounding boxes (e.g.,three-sided). At 304, edgelet detection distributions are performed onhorizontal and vertical bounding box lines. At 306, peak extraction isperformed on the edgelet distributions. At 308, entrance candidates arederived. At 310, the derived entrance candidates are classified usingscores based on at least reflection symmetry and color features. At 312,joint façade fusion is performed in 3D space to resolve conflicts andconsider constraints.

FIG. 4 illustrates an alternative method in accordance with thedisclosed architecture. At 400, images of structure entrances in anoutdoor scene are received. At 402, entrance hypotheses are generatedbased on entrance locations and gradient lines of the imaged structureentrances. At 404, entrance features of the entrance locations aredetected using the entrance hypotheses. At 406, a joint model ofcandidate entrances and entrance constraints among the structureentrances is created based on the entrance features.

The method can further comprise creating the joint model on a per-facadebasis. The method can further comprise exploiting constraints betweendifferent doors of a same facade. The method can further comprisegenerating the entrance hypotheses based on edgelet detection anddistribution. The method can further comprise projecting an entrancelikelihood of all candidate entrances into a three-dimensional space toresolve conflicts. The method can further comprise extracting featuresof each candidate entrance and each opening and classifying eachcandidate and each opening. The method can further comprise processingreflection symmetry for entrance detection.

FIG. 5 illustrates yet another alternative method in accordance with thedisclosed architecture. The method can be realized in acomputer-readable storage medium comprising computer-executableinstructions that when executed by a microprocessor, cause themicroprocessor to perform the following acts.

At 500, entrance hypotheses are generated based on extraction ofcandidate entrances from a street-side image of structure entrances andopenings of multiple views. At 502, the structure entrances and openingsare classified to differentiate the structure entrances from theopenings. At 504, all candidate entrances are projected intomulti-dimensional space to resolve conflicts.

The method can further comprise generating the entrance hypotheses on aper-facade basis. The method can further comprise exploiting constraintsbetween different doors of a same facade. The method can furthercomprise generating the entrance hypotheses based on edgelet detectionand distribution. The method can further comprise extracting features ofeach candidate entrance and each opening and classifying each candidateand each opening.

As used in this application, the terms “component” and “system” areintended to refer to a computer-related entity, either hardware, acombination of software and tangible hardware, software, or software inexecution. For example, a component can be, but is not limited to,tangible components such as a microprocessor, chip memory, mass storagedevices (e.g., optical drives, solid state drives, and/or magneticstorage media drives), and computers, and software components such as aprocess running on a microprocessor, an object, an executable, a datastructure (stored in a volatile or a non-volatile storage medium), amodule, a thread of execution, and/or a program.

By way of illustration, both an application running on a server and theserver can be a component. One or more components can reside within aprocess and/or thread of execution, and a component can be localized onone computer and/or distributed between two or more computers. The word“exemplary” may be used herein to mean serving as an example, instance,or illustration. Any aspect or design described herein as “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs.

Referring now to FIG. 6, there is illustrated a block diagram of acomputing system 600 that executes entrance detection from street-sideimages in accordance with the disclosed architecture. However, it isappreciated that the some or all aspects of the disclosed methods and/orsystems can be implemented as a system-on-a-chip, where analog, digital,mixed signals, and other functions are fabricated on a single chipsubstrate.

In order to provide additional context for various aspects thereof, FIG.6 and the following description are intended to provide a brief, generaldescription of the suitable computing system 600 in which the variousaspects can be implemented. While the description above is in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that a novelembodiment also can be implemented in combination with other programmodules and/or as a combination of hardware and software.

The computing system 600 for implementing various aspects includes thecomputer 602 having microprocessing unit(s) 604 (also referred to asmicroprocessor(s) and processor(s)), a computer-readable storage mediumsuch as a system memory 606 (computer readable storage medium/media alsoinclude magnetic disks, optical disks, solid state drives, externalmemory systems, and flash memory drives), and a system bus 608. Themicroprocessing unit(s) 604 can be any of various commercially availablemicroprocessors such as single-processor, multi-processor, single-coreunits and multi-core units of processing and/or storage circuits.Moreover, those skilled in the art will appreciate that the novel systemand methods can be practiced with other computer system configurations,including minicomputers, mainframe computers, as well as personalcomputers (e.g., desktop, laptop, tablet PC, etc.), hand-held computingdevices, microprocessor-based or programmable consumer electronics, andthe like, each of which can be operatively coupled to one or moreassociated devices.

The computer 602 can be one of several computers employed in adatacenter and/or computing resources (hardware and/or software) insupport of cloud computing services for portable and/or mobile computingsystems such as wireless communications devices, cellular telephones,and other mobile-capable devices. Cloud computing services, include, butare not limited to, infrastructure as a service, platform as a service,software as a service, storage as a service, desktop as a service, dataas a service, security as a service, and APIs (application programinterfaces) as a service, for example.

The system memory 606 can include computer-readable storage (physicalstorage) medium such as a volatile (VOL) memory 610 (e.g., random accessmemory (RAM)) and a non-volatile memory (NON-VOL) 612 (e.g., ROM, EPROM,EEPROM, etc.). A basic input/output system (BIOS) can be stored in thenon-volatile memory 612, and includes the basic routines that facilitatethe communication of data and signals between components within thecomputer 602, such as during startup. The volatile memory 610 can alsoinclude a high-speed RAM such as static RAM for caching data.

The system bus 608 provides an interface for system componentsincluding, but not limited to, the system memory 606 to themicroprocessing unit(s) 604. The system bus 608 can be any of severaltypes of bus structure that can further interconnect to a memory bus(with or without a memory controller), and a peripheral bus (e.g., PCI,PCIe, AGP, LPC, etc.), using any of a variety of commercially availablebus architectures.

The computer 602 further includes machine readable storage subsystem(s)614 and storage interface(s) 616 for interfacing the storagesubsystem(s) 614 to the system bus 608 and other desired computercomponents and circuits. The storage subsystem(s) 614 (physical storagemedia) can include one or more of a hard disk drive (HDD), a magneticfloppy disk drive (FDD), solid state drive (SSD), flash drives, and/oroptical disk storage drive (e.g., a CD-ROM drive DVD drive), forexample. The storage interface(s) 616 can include interface technologiessuch as EIDE, ATA, SATA, and IEEE 1394, for example.

One or more programs and data can be stored in the memory subsystem 606,a machine readable and removable memory subsystem 618 (e.g., flash driveform factor technology), and/or the storage subsystem(s) 614 (e.g.,optical, magnetic, solid state), including an operating system 620, oneor more application programs 622, other program modules 624, and programdata 626.

The operating system 620, one or more application programs 622, otherprogram modules 624, and/or program data 626 can include items andcomponents of the system 100 of FIG. 1, items and components of theimage 200 of FIG. 2, and the methods represented by the flowcharts ofFIGS. 3-5, for example.

Generally, programs include routines, methods, data structures, othersoftware components, etc., that perform particular tasks, functions, orimplement particular abstract data types. All or portions of theoperating system 620, applications 622, modules 624, and/or data 626 canalso be cached in memory such as the volatile memory 610 and/ornon-volatile memory, for example. It is to be appreciated that thedisclosed architecture can be implemented with various commerciallyavailable operating systems or combinations of operating systems (e.g.,as virtual machines).

The storage subsystem(s) 614 and memory subsystems (606 and 618) serveas computer readable media for volatile and non-volatile storage ofdata, data structures, computer-executable instructions, and so on. Suchinstructions, when executed by a computer or other machine, can causethe computer or other machine to perform one or more acts of a method.Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose microprocessor device(s) to perform a certainfunction or group of functions. The computer executable instructions maybe, for example, binaries, intermediate format instructions such asassembly language, or even source code. The instructions to perform theacts can be stored on one medium, or could be stored across multiplemedia, so that the instructions appear collectively on the one or morecomputer-readable storage medium/media, regardless of whether all of theinstructions are on the same media.

Computer readable storage media (medium) exclude (excludes) propagatedsignals per se, can be accessed by the computer 602, and includevolatile and non-volatile internal and/or external media that isremovable and/or non-removable. For the computer 602, the various typesof storage media accommodate the storage of data in any suitable digitalformat. It should be appreciated by those skilled in the art that othertypes of computer readable medium can be employed such as zip drives,solid state drives, magnetic tape, flash memory cards, flash drives,cartridges, and the like, for storing computer executable instructionsfor performing the novel methods (acts) of the disclosed architecture.

A user can interact with the computer 602, programs, and data usingexternal user input devices 628 such as a keyboard and a mouse, as wellas by voice commands facilitated by speech recognition. Other externaluser input devices 628 can include a microphone, an IR (infrared) remotecontrol, a joystick, a game pad, camera recognition systems, a styluspen, touch screen, gesture systems (e.g., eye movement, body poses suchas relate to hand(s), finger(s), arm(s), head, etc.), and the like. Theuser can interact with the computer 602, programs, and data usingonboard user input devices 630 such a touchpad, microphone, keyboard,etc., where the computer 602 is a portable computer, for example.

These and other input devices are connected to the microprocessingunit(s) 604 through input/output (I/O) device interface(s) 632 via thesystem bus 608, but can be connected by other interfaces such as aparallel port, IEEE 1394 serial port, a game port, a USB port, an IRinterface, short-range wireless (e.g., Bluetooth) and other personalarea network (PAN) technologies, etc. The I/O device interface(s) 632also facilitate the use of output peripherals 634 such as printers,audio devices, camera devices, and so on, such as a sound card and/oronboard audio processing capability.

One or more graphics interface(s) 636 (also commonly referred to as agraphics processing unit (GPU)) provide graphics and video signalsbetween the computer 602 and external display(s) 638 (e.g., LCD, plasma)and/or onboard displays 640 (e.g., for portable computer). The graphicsinterface(s) 636 can also be manufactured as part of the computer systemboard.

The computer 602 can operate in a networked environment (e.g., IP-based)using logical connections via a wired/wireless communications subsystem642 to one or more networks and/or other computers. The other computerscan include workstations, servers, routers, personal computers,microprocessor-based entertainment appliances, peer devices or othercommon network nodes, and typically include many or all of the elementsdescribed relative to the computer 602. The logical connections caninclude wired/wireless connectivity to a local area network (LAN), awide area network (WAN), hotspot, and so on. LAN and WAN networkingenvironments are commonplace in offices and companies and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network such as the Internet.

When used in a networking environment the computer 602 connects to thenetwork via a wired/wireless communication subsystem 642 (e.g., anetwork interface adapter, onboard transceiver subsystem, etc.) tocommunicate with wired/wireless networks, wired/wireless printers,wired/wireless input devices 644, and so on. The computer 602 caninclude a modem or other means for establishing communications over thenetwork. In a networked environment, programs and data relative to thecomputer 602 can be stored in the remote memory/storage device, as isassociated with a distributed system. It will be appreciated that thenetwork connections shown are exemplary and other means of establishinga communications link between the computers can be used.

The computer 602 is operable to communicate with wired/wireless devicesor entities using the radio technologies such as the IEEE 802.xx familyof standards, such as wireless devices operatively disposed in wirelesscommunication (e.g., IEEE 802.11 over-the-air modulation techniques)with, for example, a printer, scanner, desktop and/or portable computer,personal digital assistant (PDA), communications satellite, any piece ofequipment or location associated with a wirelessly detectable tag (e.g.,a kiosk, news stand, restroom), and telephone. This includes at leastWi-Fi™ (used to certify the interoperability of wireless computernetworking devices) for hotspots, WiMax, and Bluetooth™ wirelesstechnologies. Thus, the communications can be a predefined structure aswith a conventional network or simply an ad hoc communication between atleast two devices. Wi-Fi networks use radio technologies called IEEE802.11x (a, b, g, etc.) to provide secure, reliable, fast wirelessconnectivity. A Wi-Fi network can be used to connect computers to eachother, to the Internet, and to wire networks (which use IEEE802.3-related technology and functions).

What has been described above includes examples of the disclosedarchitecture. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible. Accordingly, the novel architecture isintended to embrace all such alterations, modifications and variationsthat fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

What is claimed is:
 1. A system, comprising: an extraction componentconfigured to extract entrance candidates from images of a facade andgenerate entrance hypotheses based on the entrance candidates; aclassification component configured to classify the entrance candidatesto differentiate facade entrances from non-entrances based on theentrance hypotheses; a multi-dimensional reasoning component configuredto project the candidates into multi-dimensional space to resolveconflicts; and at least one microprocessor configured to executecomputer-executable instructions in a memory associated with theextraction component, classification component, and multi-dimensionalreasoning component.
 2. The system of claim 1, wherein the extractioncomponent generates the entrance hypotheses based on processing of pairsof facade locations along gradient lines in a transverse direction. 3.The system of claim 1, wherein the classification component employsdiscriminative image features related to entrances, the features includeentrance symmetry and entrance color.
 4. The system of claim 1, furthercomprising a modeling component configured to formulate a joint modelfor entrances on a given facade, the joint model enables the utilizationof physical constraints between different entrances of a same facade toderive an optimum set of entrances for the given facade.
 5. The systemof claim 1, wherein the entrance candidates are specified by boundingpolygons where polygon line segmentation is determined using verticaland horizontal edge detection.
 6. The system of claim 1, wherein theimages correspond to a street-view of the facade.
 7. The system of claim1, wherein the classification component processes entrance candidatesfrom different images independently and the multi-dimensional reasoningcomponent resolves conflicts between different views of the entrancecandidates using joint reasoning in 3D space.
 8. The system of claim 1,wherein the classification component classifies the candidate entrancesbased on groundtruth matching and an entrance or non-entrance label. 9.A method, comprising acts of: receiving images of structure entrances inan outdoor scene; generating entrance hypotheses based on entrancelocations and gradient lines of the imaged structure entrances;detecting entrance features of the entrance locations using the entrancehypotheses; and creating a joint model of candidate entrances andentrance constraints among the structure entrances based on the entrancefeatures.
 10. The method of claim 9, further comprising creating thejoint model on a per-facade basis.
 11. The method of claim 9, furthercomprising exploiting constraints between different doors of a samefacade.
 12. The method of claim 9, further comprising generating theentrance hypotheses based on edgelet detection and distribution.
 13. Themethod of claim 9, further comprising projecting an entrance likelihoodof all candidate entrances into a three-dimensional space to resolveconflicts.
 14. The method of claim 9, further comprising extractingfeatures of each candidate entrance and each opening and classifyingeach candidate and each opening.
 15. The method of claim 9, furthercomprising processing reflection symmetry for entrance detection.
 16. Acomputer-readable hardware storage medium comprising computer-executableinstructions that when executed by a microprocessor, cause themicroprocessor to perform acts of: generating entrance hypotheses basedon extraction of candidate entrances from a street-side image ofstructure entrances and openings of multiple views; classifying thestructure entrances and openings to differentiate the structureentrances from the openings; and projecting all candidate entrances intomulti-dimensional space to resolve conflicts.
 17. The computer-readablehardware storage medium of claim 16, further comprising generating theentrance hypotheses on a per-facade basis.
 18. The computer-readablehardware storage medium of claim 16, further comprising exploitingconstraints between different doors of a same facade.
 19. Thecomputer-readable hardware storage medium of claim 16, furthercomprising generating the entrance hypotheses based on edgelet detectionand distribution.
 20. The computer-readable hardware storage medium ofclaim 16, further comprising extracting features of each candidateentrance and each opening and classifying each candidate and eachopening.